电动汽车充电需求预测
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摘要
为描述电动汽车的功率需求,在分析国外统计数据的基础上,分析了电动汽车初始充电时间和日行驶距离概率的分布,建立了电动汽车日充电曲线的预测模型,得出电动汽车功率需求求解方法。为比较快充与慢充对配电网的影响,在IEEE-RBTS Bus2可靠性测试系统中对上述模型进行算例分析,比较了不同方案下区域内电动汽车可允许充电的最大数量。结果表明,慢充对负荷影响较小,且可以使配电网容纳更多电动汽车。
To describe the power demand of electric vehicles,based on the analysis of foreign statistical data,the distribution of initial charging time and daily driving distance probability of electric vehicles is analyzed,the forecasting model of daily charging curve for electric vehicles is established,and the power demand solution method of electric vehicles is obtained. In order to compare the impacts of fast charging and slow charging on distribution network,the model is analyzed in the IEEE-RBTS Bus2 reliability test system,and the maximum allowable charging capacity of electric vehicles in different schemes is compared. The results show that slow charging has less impact on load and can make the distribution network accommodate more electricvehicles.
To describe the power demand of electric vehicles,based on the analysis of foreign statistical data,the distribution of initial charging time and daily driving distance probability of electric vehicles is analyzed,the forecasting model of daily charging curve for electric vehicles is established,and the power demand solution method of electric vehicles is obtained. In order to compare the impacts of fast charging and slow charging on distribution network,the model is analyzed in the IEEE-RBTS Bus2 reliability test system,and the maximum allowable charging capacity of electric vehicles in different schemes is compared. The results show that slow charging has less impact on load and can make the distribution network accommodate more electricvehicles.
引文
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[2]杨冰,戴静.电动汽车充电需求及充电设施优化配置方法研究[J].湖北电力,2018,42(5):62-69.YANG Bing,DAI Jing. Study on power-charging demand of electric vehicles and optimized configuration of charging facilities[J].Hubei Electric Power,2018,42(5):62-69.
[3]罗亮.含分布式电源和电动汽车的配电网重构[J].电力学报,2019,34(2):109-116.LUO Liang. Distribution network reconstruction including distributed power supply and electric vehicles[J]. Journal of Electric Power,2019,34(2):109-116.
[4]姜欣,冯永涛,熊虎,等.基于出行概率矩阵的电动汽车充电站规划[J/OL].电工技术学报:1-11.[2019-04-16].https://doi. org/10. 19595/j. cnki. 1000-6753. tces. L80131.JIANG Xin,FENG Yongtao,XIONG Hu,et al. Electric vehicles charging station planning based on travel probability matrix[J/OL]. Transactions of China Electrotechnical Society:1-11.[2019-04-16]. https://doi. org/10. 19595/j. cnki. 1000-6753. tces. L80131.
[5]钱甜甜,李亚平,郭晓蕊,等.基于时空活动模型的电动汽车充电功率计算和需求响应潜力评估[J].电力系统保护与控制,2018,46(23):127-134.QIAN Tiantian,Li Yaping,GUO Xiaorui,et al. Calculation of electric vehicle charging power and evaluation of demand response potential based on spatial and temporal activity model[J]. Power System Protection and Control,2018,46(23):127-134.
[6]单知非,孙宇轩,张翔,等.电动汽车充放电优化管理分析[J/OL].电子科技,2019(6):1-7.[2019-01-06].http://kns. cnki. net/kcms/detail/61. 1291. TN. 20181220.1048. 076. html.SHAN Zhifei,SUN Yuxuan,ZHANG Xiang,et al. Optimal management analysis of electric vehicles charging and discharge[J/OL]. Electronic Science and Technology,2019(6):1-7.[2019-01-06]. http://kns. cnki. net/kcms/detail/61. 1291.TN. 20181220. 1048. 076. html.
[7]陈丽丹,张尧,ANTONIO F.融合多源信息的电动汽车充电负荷预测及其对配电网的影响[J].电力自动化设备,2018,38(12):1-10.CHEN Lidan,ZHANG Yao,ANTONIO F. Charging load forecasting of electric vehicles based on multi-source information fusion and its influence on distribution network[J]. Electric Power Automation Equipment,2018,38(12):1-10.
[8]刘锴,孙小慧,左志.电动汽车充电站布局优化方法研究综述[J].武汉理工大学学报(交通科学与工程版),2015,39(3):523-528.LIU Kai,SUN Xiaohui,ZUO Zhi. Review on location optimization of recharging stations for electric vehicles[J]. Journal of Wuhan University of Technology(Transportation Science and Engineering),2015,39(3):523-528.
[9]陈舒婷.对电动汽车智能充电的思考[J].科技风,2018(33):200.CHEN Shuting. Reflections on intelligent charging of electric vehicle[J]. Technology Wind,2018(33):200.
[10]罗卓伟,胡泽春,宋永华,等.电动汽车充电负荷计算方法[J].电力系统自动化,2011,35(14):36-42.LUO Zhuowei,HU Zechun,SONG Yonghua,et al. Study on plug-in electric vehicles charging load calculating[J]. Automation of Electric Power Systems,2011,35(14):36-42.
[11] STEEN D,TUAN L A,CARLSON O,et al. Assessment of electric vehicle charging scenarios based on demographical data[J].IEEE Trans. on Smart Grid,2012,3(3):1457-1468.
[12] ASHTARI A,BIBEAU E,SHAHIDINEJAD S,et al. PEV charging profile prediction and analysis based on vehicle usage data[J].IEEE Trans. on Smart Grid,2012,3(1):341-350.
[13] GUUS B,WOUTER B,NANDA P,et al. Predicting electric vehicle charging demand using mixed generalized extreme value models with panel effects[J]. Procedia Computer Science,2018,130:549-556.
[14] ARIAS M B,BAE S W. Electric vehicle charging demand forecasting model based on big data technologies[J]. Applied Energy,2016,183:327-339.